20 beste vacatures voor de functie van ml in waterloo (nieuwe medewerkers gezocht)

AI Large Language Model (LLM) Technology Architect
Accenture —Brussel
- Voltijds
- Master
- Bachelor
- Machine learning
Data & AI Engineer
COSMOTE GLOBAL SOLUTIONS NV —Brussel
- Azure
- DevOps
- Artificiële intelligentie
Snel solliciteren
AI Strategy Lead
Degroof Petercam —Bruxelles Etterbeek
- Computerwetenschappen
- Informatica
- Master
BE - Senior Data Science Engineer
Collaboration Betters the World —Brussel
- Voltijds
- Computerwetenschappen
- Azure
- Doctoraat
Consultant Data Engineer
Sia —Brussel
- Voltijds
- Hybride werken
- Azure
- DevOps
- Master
Snel solliciteren
Python Engineer / Test Automation Specialist
Christelijke Mutualiteit —Schaarbeek
- Azure
- Engels
- SQL
- Openbaar vervoer vergoeding
- GSM-vergoeding
HPE Labs - Postdoctoral Research Scientist: Generative AI for Photonic and Analog Electronic Design Automation
Hewlett Packard Enterprise —Diegem
- Voltijds
- Computerwetenschappen
- Doctoraat
- Informatica
AI Security Specialist
Elia —Brussel
- Engels
- Master
- Machine learning
Data Engineer
Deloitte —Zaventem
- Deeltijds
- Computerwetenschappen
- Azure
- Informatica
Data-Analyse Platform Architect (DMOW)
Vlaanderen Connect. —Brussel
- € 3.920 - € 5.300 per maand
- Voltijds
- Azure
- Master
- SQL
- Thuiswerkopties
- Pensioensparen
Decision Intelligence Developer
Deloitte —Zaventem
- Deeltijds
- Azure
- ERP systemen
- Databases
Digital Solution Architect
Trusted Advisors —Brussel
- Engels
- Master
Senior AI Product Engineer
TechBiz Global GmbH —Brussel
- Voltijds
- Thuiswerk
- Azure
- AWS
- Bachelor
Snel solliciteren
Senior AI Engineer - Consultant
Orange Business —Evere
- Voltijds
- Azure
- DevOps
- ERP systemen
Senior Pricing Actuary - Non-Life Individuals
Belfius —Brussel
- Microsoft Excel
- Azure
- Master
- Pensioensparen
AI Architect - AI and Data - Industries
EY —Diegem
- Azure
- ERP systemen
- Master
- Openbaar vervoer vergoeding
AI and Digital Quality Engineer
Deloitte —Zaventem
- Deeltijds
- Machine learning
- JavaScript
- Scripting
Cybersecurity Consultant - DevSecOps / AI Security
Capgemini Sogeti —Diegem
- Azure
- Master
- AWS
Expert Project Manager Digitalisation - Sorting
bpost —Neder-Over-Heembeek
- Engels
- Master
- Entrepôt
- GSM-vergoeding
Generative AI Engineer
Proximus —Bruxelles Schaarbeek
- Voltijds
- Computerwetenschappen
- Azure
- Doctoraat

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AI Large Language Model (LLM) Technology Architect

Accenture -
Brussel

Nu solliciteren

Vacaturegegevens

Voltijds

Kwalificaties

Master
Bachelor
Machine learning
Deep learning
NLP
Artificiële intelligentie
Python

Volledige vacaturetekst

AI Large Language Model (LLM) Technology Architect

Career Level: Associate Manager / Specialist

Location: London

YOU ARE

As a hands-on AI/LLM Architect, you will be at the heart of designing and building advanced AI systems that power the modern enterprise. This is a deeply technical, hands-on role — you will spend the majority of your time in the architecture and engineering of real-world AI solutions across classical machine learning, generative AI, and agentic systems, delivering these within active client engagements.

You will translate requirements into concrete architecture decisions: selecting design patterns, evaluating and benchmarking technical frameworks, assembling reusable components, and making deliberate technology choices that balance innovation with enterprise-grade reliability. You will design and build AI agent architectures — including multi-agent orchestration, tool use, skills use, and memory systems — and work hands-on with foundation models through fine-tuning, retrieval-augmented generation (RAG), and custom model integration. A part of your work will also involve engineering the AI context layer that makes these systems intelligent in practice — connecting enterprise knowledge bases, structured and unstructured data sources, and domain-specific content so that AI outputs are grounded, accurate, and relevant to the client's business. You will design and validate systems against enterprise non-functional requirements across security, observability, governance, performance, and scalability. A core output of this role is the production of tangible engineering and architecture deliverables. This means writing and owning software components — building, integrating, and testing AI system modules as a practitioner — alongside producing detailed architecture artifacts including architecture decision records (ADRs), component diagrams, data flow diagrams, and integration specifications that guide and enable broader engineering teams.

You will work with cross-functional delivery teams alongside data engineers, ML engineers, and application developers, and this role is an opportunity to develop deep expertise across the full AI architecture stack, sharpen your engineering instincts on complex, real-world problems, and build a foundation for growing into a lead or principal architect over time.

THE WORK

Independently design, build, and deliver software components across the AI architecture — owning them end to end from design through implementation, integration, and testing as a hands-on practitioner

Design and build AI agent architectures — including individual agents, their prompts, tools, and skills, multi-agent orchestration, and memory systems — making deliberate design pattern and technology choices

Design and implement agent orchestration patterns that handle task handoffs, communication, state management, and error recovery, validating them through hands-on prototyping

Evaluate multiple design options and technical approaches, making deliberate, justified design choices that balance capability, cost efficiency, performance, and enterprise-grade reliability

Design, build, and run evaluation strategies and harnesses that measure agent and system quality on metrics such as accuracy, relevance, and faithfulness, translating findings into design improvements

Architect and implement foundation model integrations — selecting the right models, invocation patterns, and customization approaches (fine-tuning, RAG, custom integration) based on capability, cost, and performance trade-offs

Design and build model adaptation and fine-tuning pipelines, applying working knowledge of transformer-based architectures to inform model selection and optimization

Design and build the AI context layer — including context graph design and ingestion pipelines that parse, chunk, enrich, and index structured and unstructured enterprise content, and the retrieval components that ground AI outputs in the client's knowledge

Build embedding, vector storage, and retrieval (semantic, hybrid, reranking) into end-to-end RAG pipelines, applying integration patterns that connect to enterprise data sources

Design and implement context assembly and memory components that manage prompts, context windows, and conversational state for grounded, accurate outputs

Identify, design, and build reusable components and solution patterns that accelerate delivery and can be templated across engagements

Design for cost efficiency and performance — optimizing model usage, inference patterns, caching, and resource utilization to meet target latency, throughput, and cost objectives

Design, build, and validate systems against enterprise non-functional requirements — implementing guardrails, prompt-injection defenses, PII handling, and access controls for security and Responsible AI

Build governance controls including versioning, audit logging, and lineage tracking, and produce the model documentation that keeps systems auditable

Build observability into systems — logging, tracing, monitoring, alerting, and cost tracking — to ensure AI solutions remain healthy, performant, and scalable in production

Produce detailed architecture artifacts — including architecture decision records (ADRs), architecture blueprints, design documents, agent orchestration and integration pattern specifications, component and data flow diagrams — that guide and enable broader engineering teams

Continuously learn, evaluate, and apply new design patterns, frameworks, and technologies across the fast-evolving AI landscape, balancing innovation with enterprise-grade reliability

Collaborate with cross-functional delivery teams — data engineers, ML engineers, and application developers — to translate requirements into concrete architecture decisions that meet stakeholder needs

EDUCATION

Bachelor's Degree or equivalent

BASIC (REQUIRED) QUALIFICATION

Proven experience in designing & deploying enterprise grade advanced ai solutions using agentic, generative and classical AI/ML using at least one cloud vendor.

Practical experience in the Agentic, LLM and Generative AI space.

Well versed in coding using python

Solid foundation in architecting and operationalizing LLM driven application architecture patterns.

Professional working experience in coding engineering, machine learning, deep learning and NLP solutions and applications.

Several years of hands on experience as a machine learning architect in the industry designing big data, machine learning. large scale analytical engineering solutions.

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Verfijn je zoekopdracht

ml vacatures in waterloo

AI Large Language Model (LLM) Technology Architect

Data & AI Engineer

AI Strategy Lead

BE - Senior Data Science Engineer

Consultant Data Engineer

Python Engineer / Test Automation Specialist

HPE Labs - Postdoctoral Research Scientist: Generative AI for Photonic and Analog Electronic Design Automation

AI Security Specialist

Data Engineer

Data-Analyse Platform Architect (DMOW)

Decision Intelligence Developer

Digital Solution Architect

Senior AI Product Engineer

Senior AI Engineer - Consultant

Senior Pricing Actuary - Non-Life Individuals

AI Architect - AI and Data - Industries

AI and Digital Quality Engineer

Cybersecurity Consultant - DevSecOps / AI Security

Expert Project Manager Digitalisation - Sorting

Generative AI Engineer

Ik wil de nieuwste vacature-alerts ontvangen voor de functie van ml in waterloo

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